Big Genetic Data Opportunities and Threats Johann Eder. Funded by GZ /0016 II/3/2013

Transcription

1 Big Genetic Data Opportunities and Threats Johann Eder Funded by GZ /0016 II/3/2013

2 Cholera Outbreak, London, 1854 John Snow showed by cluster analysis that contaminated water, not air, spread cholera

3 Clusters of Cholera Outbreak, London,

4 Statistics and Data Mining in Medicine Symptoms > Symptoms [p] Symptoms: everything we can measure about a person Bodily characteristics Lab data Observation Tests Environmental data, l ife style, etc 4

5 Symptoms > Symptoms [p] Goals of research: increase propability p (better diagnosis) Search forcausesofdiseases Commonalities of diseased individuals Differences between diseased and non diseased Search for the effects of treatments 5

6 What is Big Data Technology BIG DATA is a set of techniques to do Data Mining on huge sets of data. Data Mining is the process that discovers new patterns embedded in large data sets. DM makes use of this information to build predictive models. DM is grounded in artificial intelligence databases statistics 6

7 Data Mining Techniques Classification Cluster analysis Association rule discovery Sequential pattern discovery Regression Deviation detection 7

8 Classification Minimal set of characteristics for diagnosing a disease Data defined in terms of symptoms, one of which is the class (disease) Mining: Find a model for class symptom as a function of the values of other(predictor) symptoms, such that (previously unseen) records can be assigned a class as accurately as possible. Application: which tests are necessary to distinguish different diseases 8

9 Classification Application: which tests are necessary to distinguish different diseases 9

10 Association Rules Objective: Identify items that occur together Support of {salsa, chip} is 80%, Support of {bread, milk} is 60% Application: Gene function, interdependencies of drugs 10

11 Goals of Big Genetic Data The nontrivial extraction of implicit, previously unknown, and potentially useful information for Diagnosis Cure Prognosis Prevention of diseases 11

12 Genomics: DNA Sequencing The complete blueprint of an individual. In perspective the time for sequencing the whole genome of an individual is less and less, and the genome of more and more individuals will be available DNA orbit animated (Zephyris, Wikipedia) 12

13 Stephens ZD, Lee SY, Faghri F, Campbell RH, Zhai C, et al. (2015) Big Data: Astronomical or Genomical?. PLoS Biol 13(7): e doi: /journal.pbio

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17 Genomics is only the start! Proteomics: About 100 thousand proteins from ~22000 genes. The expression of proteins is tissue/cell dependent. Well being corresponds to the correct amount of proteins in a tissue/cell. Metabolomics: Genomics tells you what could happen. Metabolomics of biological fluids and tissues/cells tells you what has happened. The metabolites are only a few thousand. 17

18 A Testimony Preventing drug related adverse effects Pharmacogenomics prescribing medications based on a person s genomic information is helping physicians avoid harmful reactions. As Mayo has embedded its available patients genomic information in the electronic health record, more than 3500 adverse reactions have been prevented in the last two years. Mayo Clinic Vice President Gianrico Farrugia, M.D. highlights impacts of individualised medicine during opening keynote at 4th annual Individualizing Medicine Conference,

19 Symptoms > Symptoms [p] Probability of acquiring a disease Life style Diet Drinking Sports Behavioural characteristics Short fused Lethargic 19

20 Whom do you entrust your genetic data? Even small changes in the conditional probability might be (economically) quite relevant 20

21 Adverse Effects of Big Data Data may be used for purposes not in the interest of the donor Marketing Insurance Employer Crime prevention Privacy is a major issue in Big Data Analytics 21

22 Discussion Big Data Genetics is a game changer in many areas Who is allowed to collect genetic data? Which are indispensible rights of the DNA/data owners For which purposes may genetic data be used? 22

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25 Privacy of Anonymized Data Removing usual identifiers from data does not warrant privacy Trackers Linking attacs Hereditary attacs 25

26 Tracker Zip Age Sex Condition F Hepatitis M Hepatitis M Flu M Flu F Cancer M Hepatitis M Flu M Flu F Cancer F Cancer F Cancer F Cancer Tracker = a special characteristic formula (used in queries). Following steps lead to a compromise: 1) COUNT(Sex=F ) = 6 2) COUNT( Sex=F or Sex=M & Age = 29 &Condition = Hepatitis) = 7 26

27 Statistical Blurring Methods In our example: Add random number to query result Query results no longer exact Sufficient for approximate answers 27

28 Example: Linking Attack Name ZIP Age Geschl. 1 Anton M 2 Berta F 3 Cesar M 4 Dora F 5 Emil M 6 Friedrich M 7 Gustav M 8 Heinrich M 9 Ida F 10 Julius M 11 Konrad M 12 Ludwig M Public directory LINK PLZ Alter Geschl. Diagnose M Hepatitis F Hepatitis M Pneumonie F Pneumonie M Krebs M Hepatitis M Pneumonie M Pneumonie F Krebs M Krebs M Krebs M Krebs Medical data 28

29 k Anonymity and l Diversity PLZ Alter Geschl. Diagnose * 40 * Hepatitis * 40 * Pneumonie * 40 * Krebs * 40 * Krebs * > 40 * Krebs * > 40 * Hepatitis * > 40 * Pneumonie * > 40 * Pneumonie * 40 * Hepatitis * 40 * Pneumonie * 40 * Krebs * 40 * Krebs Hide individuals in a crowd Linking attacks no longer possible Information loss 4 anonymous, 3 diverse table 29

30 Heredity Attacks Exploits hereditary relationships between DNA of individuals Can identify anonymized genetic data of other individuals Can reason about DNA of ofsprings and successors 30

31 Hereditary attacks Melissa Gymrek, Amy L. McGuire, David Golan, Eran Halperin, Yaniv Erlich: Identifying Personal Genomes by Surname Inference Science 18 January

32 Dilemma Real anonymization leads to considerable loss of data For research precise data is necessary 32

33 Example Policy for Biobanks Individual is data owner Can grant and revoke rights to use data for research Informed Consent necessary Access is only granted in truly anonymized form Researchers search for available datasets and materials K anonymity or statistical blurring Detailed data is passed to researchers only after approval of an ethics committee Data needs and data security are part of the assessment 33

34 Conclusions Big Data Analyis offers fantastic possibilities for progress in medicine and health care Worth of donating data for medical research Privacy is a major concern and not trivial Consider whom to entrust your data Better prognosis will be game changer in many areas Start societal discussion now 34

35 THANK YOU! Johann Eder Institut für Informatik Systeme Alpen Adria Universität Klagenfurt Universitätsstraße Klagenfurt Put CP Logo here Funded by BBRMI.at Neue Stiftingtalstraße 2/B/6 A 8010 Graz Austria 35